{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "\n",
    "import torch\n",
    "\n",
    "from scipy.io import loadmat\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.somersault1824.com/wp-content/uploads/2015/02/color-blindness-palette-e1423327633855.png\n",
    "cbcolors={1:(0,0,0),\n",
    "          2:(0,73,73),\n",
    "          3:(0,146,146),\n",
    "          4:(255,109,182),\n",
    "          5:(255,182,119),\n",
    "          6:(73,0,146),\n",
    "          7:(0,109,219),\n",
    "          8:(182,109,255),\n",
    "          9:(109,182,255),\n",
    "          10:(182,219,255),\n",
    "          11:(146,0,0),\n",
    "          12:(146,73,0),\n",
    "          13:(219,209,0),\n",
    "          14:(36,255,36),\n",
    "          15:(255,255,109)}\n",
    "cbcolors={k:(v[0]/255,v[1]/255,v[2]/255) for k,v in cbcolors.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device('cuda:0' if use_cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot='baselines'\n",
    "plot='variants'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "if plot=='baselines':\n",
    "    methods = ['hesaff',\n",
    "               'hesaffnet', \n",
    "               'delf', \n",
    "               'superpoint',\n",
    "               'd2-net',\n",
    "               'r2d2',\n",
    "               'ncnet.sparsencnet_3200_hard_soft_1k']\n",
    "    \n",
    "    names = ['Hes. Aff. + Root-SIFT',\n",
    "             'HAN + HN++', \n",
    "             'DELF',\n",
    "             'SuperPoint',\n",
    "             'D2-Net Trained', \n",
    "             'R2D2',\n",
    "             'Sparse-NCNet, hard+soft, r=3200']\n",
    "    \n",
    "    colors=[cbcolors[3],\n",
    "            cbcolors[14],\n",
    "            cbcolors[7],\n",
    "            cbcolors[10],\n",
    "            cbcolors[4],\n",
    "            cbcolors[8],\n",
    "            cbcolors[1]]\n",
    "                \n",
    "    linestyles = ['--', '--', '-', '-.',\n",
    "                  '-.', '-', '-']\n",
    "    \n",
    "    markers = ['^','o','|','s','x','v','']\n",
    "\n",
    "    plot_name='hseq_ncnet_vs_baselines'\n",
    "    \n",
    "elif plot=='variants':\n",
    "    methods = ['ncnet.sparsencnet_800_noreloc_2k',\n",
    "               'ncnet.densencnet_800_noreloc_2k',\n",
    "               'ncnet.sparsencnet_1600_hard_2k',\n",
    "               'ncnet.densencnet_1600_hard_2k',\n",
    "               'ncnet.sparsencnet_1600_hard_soft_2k',\n",
    "               'ncnet.sparsencnet_3200_hard_soft_2k']\n",
    "    \n",
    "    names = ['Sparse-NCNet, r=800',\n",
    "             'NCNet, r=800',\n",
    "             'Sparse-NCNet, hard, r=1600',\n",
    "             'NCNet, hard, r=1600',\n",
    "             'Sparse-NCNet, hard+soft, r=1600',\n",
    "             'Sparse-NCNet, hard+soft, r=3200']\n",
    "\n",
    "    markers = ['^','o','|','s','x','']\n",
    "    \n",
    "    colors=[cbcolors[3],\n",
    "            cbcolors[14],\n",
    "            cbcolors[7],\n",
    "            cbcolors[10],\n",
    "            cbcolors[4],\n",
    "            cbcolors[1]]\n",
    "                \n",
    "    linestyles = ['--', '--', '-.', '-.',\n",
    "                  '-', '-', '-', '-']\n",
    "    \n",
    "    plot_name='hseq_ncnet_variants'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change here if you want to use top K or all features.\n",
    "top_k = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_i = 52\n",
    "n_v = 56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = '../datasets/hpatches/'\n",
    "dataset_path = os.path.join(base_path,'hpatches-sequences-release')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "lim = [1, 15]\n",
    "rng = np.arange(lim[0], lim[1] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mnn_matcher(descriptors_a, descriptors_b):\n",
    "    device = descriptors_a.device\n",
    "    sim = descriptors_a @ descriptors_b.t()\n",
    "    nn12 = torch.max(sim, dim=1)[1]\n",
    "    nn21 = torch.max(sim, dim=0)[1]\n",
    "    ids1 = torch.arange(0, sim.shape[0], device=device)\n",
    "    mask = (ids1 == nn21[nn12])\n",
    "    matches = torch.stack([ids1[mask], nn12[mask]])\n",
    "    return matches.t().data.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark_features_ncnet(read_feats):\n",
    "    seq_names = sorted(os.listdir(dataset_path))\n",
    "\n",
    "    n_feats = []\n",
    "    n_matches = []\n",
    "    seq_type = []\n",
    "    i_err = {thr: 0 for thr in rng}\n",
    "    v_err = {thr: 0 for thr in rng}\n",
    "\n",
    "    for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):\n",
    "        for im_idx in range(2, 7):\n",
    "            keypoints_a, keypoints_b = read_feats(seq_name, im_idx)            \n",
    "\n",
    "            if im_idx==2:\n",
    "                n_feats.append(keypoints_a.shape[0])\n",
    "            n_feats.append(keypoints_b.shape[0])\n",
    "\n",
    "            homography = np.loadtxt(os.path.join(dataset_path, seq_name, \"H_1_\" + str(im_idx)))\n",
    "            \n",
    "            pos_a = keypoints_a\n",
    "            pos_a_h = np.concatenate([pos_a, np.ones([pos_a.shape[0], 1])], axis=1)\n",
    "            pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))\n",
    "            pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2 :]\n",
    "\n",
    "            pos_b = keypoints_b\n",
    "\n",
    "            dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))\n",
    "\n",
    "            n_matches.append(keypoints_a.shape[0])\n",
    "            seq_type.append(seq_name[0])\n",
    "            \n",
    "            if dist.shape[0] == 0:\n",
    "                dist = np.array([float(\"inf\")])\n",
    "            \n",
    "            for thr in rng:\n",
    "                if seq_name[0] == 'i':\n",
    "                    i_err[thr] += np.mean(dist <= thr)\n",
    "                else:\n",
    "                    v_err[thr] += np.mean(dist <= thr)\n",
    "    \n",
    "    seq_type = np.array(seq_type)\n",
    "    n_feats = np.array(n_feats)\n",
    "    n_matches = np.array(n_matches)\n",
    "    \n",
    "    return i_err, v_err, [seq_type, n_feats, n_matches]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def benchmark_features(read_feats):\n",
    "    seq_names = sorted(os.listdir(dataset_path))\n",
    "\n",
    "    n_feats = []\n",
    "    n_matches = []\n",
    "    seq_type = []\n",
    "    i_err = {thr: 0 for thr in rng}\n",
    "    v_err = {thr: 0 for thr in rng}\n",
    "\n",
    "    for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):\n",
    "        keypoints_a, descriptors_a = read_feats(seq_name, 1)\n",
    "        n_feats.append(keypoints_a.shape[0])\n",
    "\n",
    "        for im_idx in range(2, 7):\n",
    "            keypoints_b, descriptors_b = read_feats(seq_name, im_idx)\n",
    "            n_feats.append(keypoints_b.shape[0])\n",
    "\n",
    "            matches = mnn_matcher(\n",
    "                torch.from_numpy(descriptors_a).to(device=device), \n",
    "                torch.from_numpy(descriptors_b).to(device=device)\n",
    "            )\n",
    "            \n",
    "            homography = np.loadtxt(os.path.join(dataset_path, seq_name, \"H_1_\" + str(im_idx)))\n",
    "            \n",
    "            pos_a = keypoints_a[matches[:, 0], : 2]             \n",
    "            pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1)\n",
    "            pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))\n",
    "            pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2 :]\n",
    "\n",
    "            pos_b = keypoints_b[matches[:, 1], : 2]\n",
    "            \n",
    "            dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))\n",
    "\n",
    "            n_matches.append(matches.shape[0])\n",
    "            seq_type.append(seq_name[0])\n",
    "            \n",
    "            if dist.shape[0] == 0:\n",
    "                dist = np.array([float(\"inf\")])\n",
    "            \n",
    "            for thr in rng:\n",
    "                if seq_name[0] == 'i':\n",
    "                    i_err[thr] += np.mean(dist <= thr)\n",
    "                else:\n",
    "                    v_err[thr] += np.mean(dist <= thr)\n",
    "    \n",
    "    seq_type = np.array(seq_type)\n",
    "    n_feats = np.array(n_feats)\n",
    "    n_matches = np.array(n_matches)\n",
    "    \n",
    "    return i_err, v_err, [seq_type, n_feats, n_matches]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary(stats):\n",
    "    seq_type, n_feats, n_matches = stats\n",
    "    print('# Features: {:f} - [{:d}, {:d}]'.format(np.mean(n_feats), np.min(n_feats), np.max(n_feats)))\n",
    "    print('# Matches: Overall {:f}, Illumination {:f}, Viewpoint {:f}'.format(\n",
    "        np.sum(n_matches) / ((n_i + n_v) * 5), \n",
    "        np.sum(n_matches[seq_type == 'i']) / (n_i * 5), \n",
    "        np.sum(n_matches[seq_type == 'v']) / (n_v * 5))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_read_function(method, extension='ppm'):\n",
    "    if method.startswith('ncnet'):\n",
    "        def read_function_ncnet(seq_name, im_idx):\n",
    "            aux = np.load(os.path.join(dataset_path, seq_name, '{}_{}.npz'.format(1,im_idx)+method[5:]))\n",
    "            assert('scores' in aux)\n",
    "            ids = np.argsort(aux['scores'])[-top_k :]\n",
    "            return aux['keypoints_A'][ids, :], aux['keypoints_B'][ids, :]\n",
    "        return read_function_ncnet\n",
    "    else:\n",
    "        def read_function(seq_name, im_idx):\n",
    "            aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method)))\n",
    "            if top_k is None:\n",
    "                return aux['keypoints'], aux['descriptors']\n",
    "            else:\n",
    "                assert('scores' in aux)\n",
    "                ids = np.argsort(aux['scores'])[-top_k :]\n",
    "                return aux['keypoints'][ids, :], aux['descriptors'][ids, :]\n",
    "        return read_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sift_to_rootsift(descriptors):\n",
    "    return np.sqrt(descriptors / np.expand_dims(np.sum(np.abs(descriptors), axis=1), axis=1) + 1e-16)\n",
    "def parse_mat(mat):\n",
    "    keypoints = mat['keypoints'][:, : 2]\n",
    "    raw_descriptors = mat['descriptors']\n",
    "    l2_norm_descriptors = raw_descriptors / np.expand_dims(np.sum(raw_descriptors ** 2, axis=1), axis=1)\n",
    "    descriptors = sift_to_rootsift(l2_norm_descriptors)\n",
    "    if top_k is None:\n",
    "        return keypoints, descriptors\n",
    "    else:\n",
    "        assert('scores' in mat)\n",
    "        ids = np.argsort(mat['scores'][0])[-top_k :]\n",
    "        return keypoints[ids, :], descriptors[ids, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "if top_k is None:\n",
    "    cache_dir = os.path.join(base_path,'cache')\n",
    "else:\n",
    "    cache_dir = os.path.join(base_path,'cache-top')\n",
    "\n",
    "if not os.path.isdir(cache_dir):\n",
    "    os.mkdir(cache_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ncnet.sparsencnet_800_noreloc_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n",
      "ncnet.densencnet_800_noreloc_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n",
      "ncnet.sparsencnet_1600_hard_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n",
      "ncnet.densencnet_1600_hard_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n",
      "ncnet.sparsencnet_1600_hard_soft_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n",
      "ncnet.sparsencnet_3200_hard_soft_2k\n",
      "Loading precomputed errors...\n",
      "# Features: 2000.000000 - [2000, 2000]\n",
      "# Matches: Overall 2000.000000, Illumination 2000.000000, Viewpoint 2000.000000\n"
     ]
    }
   ],
   "source": [
    "for method in methods:\n",
    "    output_file = os.path.join(cache_dir, method + '.npy')\n",
    "    print(method)\n",
    "    if method == 'hesaff':\n",
    "        read_function = lambda seq_name, im_idx: parse_mat(loadmat(os.path.join(dataset_path, seq_name, '%d.ppm.hesaff' % im_idx), appendmat=False))\n",
    "    else:\n",
    "        read_function = generate_read_function(method)\n",
    "    if method.startswith('ncnet'):\n",
    "        benchmark_function=benchmark_features_ncnet\n",
    "    else:\n",
    "        benchmark_function=benchmark_features\n",
    "    if os.path.exists(output_file):        \n",
    "        print('Loading precomputed errors...')\n",
    "        errors[method] = np.load(output_file, allow_pickle=True)\n",
    "    else:\n",
    "        errors[method] = benchmark_function(read_function)\n",
    "        np.save(output_file, errors[method])\n",
    "    summary(errors[method][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_lim = [1, 10]\n",
    "plt_rng = np.arange(plt_lim[0], plt_lim[1] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(linestyles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'fontname':'CMU Bright'}\n",
    "\n",
    "plt.rc('axes', titlesize=25)\n",
    "plt.rc('axes', labelsize=25)\n",
    "#plt.rc('text', usetex=True)\n",
    "plt.rc('text', usetex=False)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    if i%2==0:\n",
    "        me = 2\n",
    "    else:\n",
    "        me=(1,2)\n",
    "    plt.plot(plt_rng, [i_err[thr] / (n_i * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)\n",
    "plt.title('Illumination',**font)\n",
    "plt.xlabel('threshold [px]',**font)\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "plt.ylabel('MMA',**font)\n",
    "plt.ylim([0, 1])\n",
    "#plt.gca().axes.set_yticklabels([])\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    if i%2==0:\n",
    "        me = 2\n",
    "    else:\n",
    "        me=(1,2)\n",
    "    plt.plot(plt_rng, [v_err[thr] / (n_v * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)\n",
    "    \n",
    "plt.title('Viewpoint',**font)\n",
    "plt.xlabel('threshold [px]',**font)\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "#plt.ylabel('MMA',**font)\n",
    "plt.ylim([0, 1])\n",
    "plt.gca().axes.set_yticklabels([])\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    if i%2==0:\n",
    "        me = 2\n",
    "    else:\n",
    "        me=(1,2)\n",
    "    plt.plot(plt_rng, [(i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)\n",
    "plt.title('Overall',**font)\n",
    "plt.xlabel('threshold [px]',**font)\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "#plt.ylabel('MMA',**font)\n",
    "plt.ylim([0, 1])\n",
    "plt.gca().set_yticklabels(\"\")\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "for i, (method, name, color, ls, ms) in enumerate(zip(methods, names, colors, linestyles, markers)):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    plt.plot([], [], color=color, ls=ls, linewidth=3, label=name, marker=ms, markersize=10, markevery=me, markeredgewidth=2)\n",
    "plt.axis('off')\n",
    "plt.legend(loc='center',prop={'size': 15, 'family':'CMU Bright'})\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4,wspace=0.1)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch1_c9",
   "language": "python",
   "name": "pytorch1_c9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
